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Robust System Design MIT Learning Objectives Introduce the concept of matrix experiments Define the balancing property and orthogonality Explain how to analyze data from matrix experiments Get some practice conducting a matrix experiment

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Robust System Design MIT ParameterDesignProblem Define a set of control factors (A,B,C…) Each factor has a set of discrete levels Some desired response h (A,B,C…) is to be maximized

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Robust System Design MIT FullFactorial Approach Try all combinations of all levels of the factors (A1B1C1, A1B1C2,...) If no experimental error, it is guaranteed to find maximum If there is experimental error, replications will allow increased certainty BUT...#experiments =#levels#control factors

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Robust System Design MIT Analysis of Variance (ANOVA) ANOVA helps to resolve the relative magnitude of the factor effects compared to the error variance Are the factor effects real or just noise? I will cover it in Lecture 7 You may want to try the Mathcad resource center under the help menu